Evaluating Macroeconomic Forecasts: A Concise Review of Some Recent Developments

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dc.contributor.authorFranses, Philip Hans
dc.contributor.authorMcAleer, Michael
dc.contributor.authorLegerstee, Rianne
dc.date.accessioned2023-06-20T09:14:31Z
dc.date.available2023-06-20T09:14:31Z
dc.date.issued2012-06
dc.descriptionJEL Classifications: C22, C51, C52, C53, E27, E37. The authors wish to thank Les Oxley, Chia-Lin Chang, an Associate Editor and two anonymous referees for detailed and helpful comments and suggestions. The second author wishes to acknowledge the financial support of the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science.
dc.description.abstractMacroeconomic forecasts are frequently produced, widely published, intensively discussed and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyse some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC) and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes non-standard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC.
dc.description.facultyFac. de Ciencias Económicas y Empresariales
dc.description.facultyInstituto Complutense de Análisis Económico (ICAE)
dc.description.refereedFALSE
dc.description.statusunpub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/15603
dc.identifier.citationBatchelor, R. (2007), Bias in Macroeconomic Forecasts, International Journal of Forecasting, 23, 189-203. Chang, C.-L., P.H. Franses and M. McAleer (2011), How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan, International Journal of Forecasting, 27, 1066-1075. Chong, Y.Y. and D.F. Hendry (1986), Econometric Evaluation of Linear Macro-Economic Models, Review of Economic Studies, 53, 671-690. Clark, T.E. and M.W. McCracken (2001), Tests of Equal Forecast Accuracy and Encompassing for Nested Models, Journal of Econometrics, 105, 85-110. Clements, M.P. and D.F. Hendry (2002) (eds.), A Companion to Economic Forecasting, Oxford: Blackwell. Diebold, F.X. and R.S. Mariano (1995), Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, 253-263. Elliott, G. and A. Timmermann (2008), Economic Forecasting, Journal of Economic Literature, 46, 3-56. Eroglu, C. and K.L. Croxton (2010), Biases in Judgmental Adjustments of Statistical Forecasts: The Role of Individual Differences, International Journal of Forecasting, 26, 116-133. Fiebig, D.G., M. McAleer and R. Bartels (1992), Properties of Ordinary Least Squares Estimators in Regression Models with Non-Spherical Disturbances, Journal of Econometrics, 54, 321-334. Fildes, R, P. Goodwin, M. Lawrence, and K. Nikolopoulos (2009), Effective Forecasting and Judgemental Adjustments: An Empirical Evaluation and Strategies for Improvement in Supply-Chain Planning, International Journal of Forecasting, 25, 3-23. Franses, P.H., H. Kranendonk, and D. Lanser (2011), One Model and Various Experts: Evaluating Dutch Macroeconomic Forecasts, International Journal of Forecasting, 27, 482-495. Franses, P.H. and R. Legerstee (2009), Properties of Expert Adjustments on Model-Based SKU-level Forecasts, International Journal of Forecasting, 25, 35-47. Franses, P.H., M. McAleer and R. Legerstee (2009), Expert Opinion Versus Expertise in Forecasting, Statistica Neerlandica, 63, 334-346. Granger, C.W.J. and P. Newbold (1986), Forecasting Economic Time Series, San Diego: Academic Press. Heij, C., D.J.C. van Dijk and P.J.F. Groenen (2011), Real-time Macroeconomic Forecasting with Leading Indicators: An Empirical Comparison, International Journal of Forecasting, 27, 466-481. McAleer, M. (1992), Efficient Estimation: The Rao-Zyskind Condition, Kruskal’s Theorem and Ordinary Least Squares, Economic Record, 68, 65-72. McAleer, M. and C.R. McKenzie (1991), When Are Two Step Estimators Efficient?, Econometric Reviews, 10, 235-252. Romer, C.D. and D.H. Romer (2004), A New Measure of Monetary Shocks: Derivation and Implications, American Economic Review, 94, 1055-1084. Romer, C.D. and D.H. Romer (2008), The FOMC Versus the Staff: Where Can Monetary Policymakers Add Value?, American Economic Review: Papers & Proceedings, 98, 230-235. Smith, J. and M. McAleer (1994), Newey-West Covariance Matrix Estimates for Models with Generated Regressors, Applied Economics, 26, 635-640. Stock, J.H. and M.W. Watson (2003), Forecasting Output abd Inflation: The Role of Asset Prices, Journal of Economic Literature, 41, 788-829. Timmermann, A. (2006), Forecast Combinations, in G. Elliott, C.W.J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, Amsterdam: North Holland. West, K.D. (1996), Asymptotic Inference About Predictive Ability, Econometrica, 64, 1067-1084.
dc.identifier.relatedurlhttps://www.ucm.es/icae
dc.identifier.urihttps://hdl.handle.net/20.500.14352/49093
dc.issue.number14
dc.language.isoeng
dc.page.total29
dc.relation.ispartofseriesDocumentos de Trabajo del Instituto Complutense de Análisis Económico (ICAE)
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.keywordMacroeconomic forecasts
dc.subject.keywordEconometric models
dc.subject.keywordHuman intuition
dc.subject.keywordBiased forecasts
dc.subject.keywordForecast performance
dc.subject.keywordForecast evaluation
dc.subject.keywordForecast comparison.
dc.subject.ucmEconometría (Economía)
dc.subject.ucmMacroeconomía
dc.subject.unesco5302 Econometría
dc.subject.unesco5307.14 Teoría Macroeconómica
dc.titleEvaluating Macroeconomic Forecasts: A Concise Review of Some Recent Developments
dc.typetechnical report
dc.volume.number2012
dspace.entity.typePublication
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